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Livius's Projects

absa-pytorch icon absa-pytorch

Aspect Based Sentiment Analysis, PyTorch Implementations. 基于方面的情感分析,使用PyTorch实现。

answerability-metric icon answerability-metric

This repository contains the script to compute the questions based on the Answerability aspect.

awesome-pytorch-list icon awesome-pytorch-list

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

casrel icon casrel

A Novel Cascade Binary Tagging Framework for Relational Triple Extraction. Accepted by ACL 2020.

chatbot icon chatbot

一个可以自己进行训练的中文聊天机器人, 根据自己的语料训练出自己想要的聊天机器人,可以用于智能客服、在线问答、智能聊天等场景。加入seqGAN版本。

deeplearning-500-questions icon deeplearning-500-questions

深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为15个章节,近20万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06

gan-ael icon gan-ael

the PyTorch implementation of paper: [Neural Response Generation via GAN with an Approximate Embedding Layer](http://www.aclweb.org/anthology/D/D17/D17-1065.pdf) Please refer to ###dev### branch to run the code.

ganhacks icon ganhacks

starter from "How to Train a GAN?" at NIPS2016

machinelearning_ng icon machinelearning_ng

吴恩达机器学习coursera课程,学习代码(2017年秋) The Stanford Coursera course on MachineLearning with Andrew Ng

models icon models

Models and examples built with TensorFlow

nqg_ass2s icon nqg_ass2s

Implementation of <Improving Neural Question Generation Using Answer Separation> by Yanghoon Kim et al., AAAI 2019

princetonalgorithms icon princetonalgorithms

Python implementations of selected Princeton Java Algorithms and Clients by Robert Sedgewick and Kevin Wayne

pytorch-adda icon pytorch-adda

A PyTorch implementation for Adversarial Discriminative Domain Adaptation

pytorch-cifar100 icon pytorch-cifar100

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet)

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